Joeyfully/Voice-Generation

Radio TTS Quick Start

This workspace fine-tunes a text-to-speech model on the dataset in data/ and writes all outputs to output/. The training and testing scripts are available at https://github.com/Lambchem/Voice-Generation

What you get

• train.py for training and automatic checkpoint saving
• eval.py for synthesizing one WAV file from text
• output/model/ for saved model checkpoints
• output/rollout/ for per-epoch audio samples
• loss.csv and loss.png in the project root for loss tracking

Requirements

You need Python with these packages available:

• torch
• torchaudio
• transformers
• datasets
• pandas
• soundfile
• matplotlib

The first run will also download the pretrained SpeechT5 model and vocoder from Hugging Face.

Data layout

The dataset should stay in the provided structure:

data/
  train-00000-of-00010.parquet
  train-00001-of-00010.parquet
  ...

The dataset metadata is described in dataset_README.md.

Train

Start training with the default settings:

python train.py

Recommended first run on Windows:

python train.py --epochs 1 --batch-size 2

Useful options:

• --max-examples limits the dataset for a quick smoke test
• --sample-text adds custom rollout prompts and may be passed multiple times
• --output-dir changes where checkpoints and samples are written

Resume training

Training resumes automatically from the latest checkpoint in output/model/ when one exists.

To force a fresh run, use:

python train.py --no-resume

Outputs after each epoch

Each epoch writes:

• output/model/epoch_XXXX/ for the checkpoint
• output/rollout/epoch_XXXX/ for generated audio samples
• loss.csv and loss.png updated in the project root
• output/model/last_checkpoint.txt pointing to the most recent checkpoint

Evaluate

Generate a WAV file from text:

python eval.py --text "Hello, this is a test."

The output is written to output/eval/.

Long text

eval.py supports long text by splitting the input into chunks, re-encoding each chunk through the tokenizer, generating audio chunk by chunk, and stitching the result into one WAV file.

If you want to tune long-form generation, try:

python eval.py --text "your long text" --chunk-token-limit 480 --chunk-gap-seconds 0.05 --maxlenratio 60

Notes

• The scripts use the pretrained microsoft/speecht5_tts model and microsoft/speecht5_hifigan vocoder by default.
• The dataset README says attribution is required when using the generated voice in interfaces that generate audio in response to user action. Refer to the voice as Jenny, and where practical, Jenny (Dioco).